NumPy (short for Numerical Python) is a popular Python library for scientific computing and data analysis. It provides support for large, multi-dimensional arrays and matrices, along with a wide range of mathematical functions to operate on these arrays. NumPy is an essential library for data scientists, machine learning practitioners, and other scientific computing applications in Python.
NumPy is open source and free to use. It is widely used in the scientific computing community due to its speed and efficiency, which is achieved through the use of optimized C code under the hood. NumPy's performance is further enhanced by its ability to interface with other programming languages such as C, C++, and Fortran.
Multi-dimensional arrays and matrices: - NumPy provides support for creating and manipulating multi-dimensional arrays and matrices, which are essential for scientific computing and data analysis.
Mathematical functions: - NumPy provides a wide range of mathematical functions, including trigonometric functions, logarithmic functions, and statistical functions.
Broadcasting: - NumPy provides a powerful broadcasting feature that allows mathematical operations to be performed on arrays of different shapes and sizes.
Linear algebra: - NumPy provides support for linear algebra operations such as matrix multiplication, matrix inversion, and eigenvalue decomposition.
Random number generation: NumPy includes a powerful random number generator that can be used for simulations and statistical analysis.
NumPy is a powerful library that provides essential tools for scientific computing and data analysis in Python. Its support for multi-dimensional arrays, mathematical functions, and linear algebra operations make it an indispensable tool for data scientists and machine learning practitioners.
NumPy provides a wide range of functions that are commonly used in scientific computing and data analysis.
numpy.array(): - Creates a new NumPy array.
numpy.arange(): - Generates a sequence of evenly spaced numbers within a specified range.
numpy.linspace(): - Generates a sequence of evenly spaced numbers within a specified interval.
numpy.reshape(): Changes the shape of an existing NumPy array.
numpy.transpose(): - Transposes a NumPy array.
numpy.dot(): - Performs matrix multiplication between two NumPy arrays.
numpy.sum(): - Computes the sum of elements in a NumPy array.
numpy.mean(): - Computes the mean of elements in a NumPy array.
numpy.std(): - Computes the standard deviation of elements in a NumPy array.
numpy.min(): - Finds the minimum value in a NumPy array.
numpy.max(): - Finds the maximum value in a NumPy array.
numpy.argmax(): - Finds the index of the maximum value in a NumPy array.
numpy.argmin(): - Finds the index of the minimum value in a NumPy array.
numpy.where(): - Returns the indices of elements in a NumPy array that meet a specified condition.
numpy.unique(): - Returns the unique elements in a NumPy array.
numpy.random.rand(): - Generates a random NumPy array with elements uniformly distributed between 0 and 1.
numpy.random.randn(): - Generates a random NumPy array with elements normally distributed with mean 0 and standard deviation 1.
These are just a few examples of the many functions provided by NumPy. By using these functions, along with many others, data scientists and machine learning practitioners can efficiently manipulate and analyze large datasets in Python.
Pandas is a popular open-source data analysis and manipulation library for the Python programming language. It provides easy-to-use data structures and data analysis tools for handling and manipulating numerical tables and time-series data.
Pandas is built on top of the NumPy library and provides an efficient implementation of data manipulation and analysis tools. It includes several data structures for efficient storage and manipulation of data, such as the DataFrame, Series, and Panel objects. Pandas also provides several functions for data cleaning, preparation, and transformation.
Data structures: - Pandas provides the DataFrame, Series, and Panel objects for efficient storage and manipulation of data.
Data cleaning and preparation: - Pandas provides several functions for cleaning, preparing, and transforming data.
Data exploration: - Pandas provides several functions for exploring and analyzing data, such as filtering, grouping, and aggregating data.
Time-series data analysis: - Pandas provides tools for efficient manipulation and analysis of time-series data.
Integration with other libraries: Pandas can be easily integrated with other libraries, such as NumPy, Matplotlib, and Scikit-learn.
NPandas provides a wide range of functions for data manipulation, cleaning, and exploration.
pandas.read_csv(): - Reads a CSV file into a Pandas DataFrame.
pandas.DataFrame(): - Creates a new DataFrame.
DataFrame.head(): - Returns the first n rows of a DataFrame.
DataFrame.tail(): - Returns the last n rows of a DataFrame.
DataFrame.describe(): - Generates descriptive statistics of a DataFrame.
DataFrame.info(): - Provides information about a DataFrame, such as data types and non-null values.
DataFrame.drop(): - Drops specified rows or columns from a DataFrame.
DataFrame.fillna(): - Fills missing values in a DataFrame with a specified value.
DataFrame.groupby(): - Groups data in a DataFrame based on specified columns.
DataFrame.merge(): - Merges two or more DataFrames based on specified columns.
DataFrame.sort_values(): - Sorts a DataFrame based on specified columns.
DataFrame.apply(): - Applies a function to each row or column of a DataFrame.
DataFrame.pivot_table(): - Creates a pivot table from a DataFrame.
DataFrame.plot(): - Plots data from a DataFrame.
DataFrame.to_csv(): - Writes a DataFrame to a CSV file.